{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6850189",
   "metadata": {},
   "source": [
    "# Graph QA\n",
    "\n",
    "This notebook goes over how to do question answering over a graph data structure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e516e3e",
   "metadata": {},
   "source": [
    "## Create the graph\n",
    "\n",
    "In this section, we construct an example graph. At the moment, this works best for small pieces of text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3849873d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.indexes import GraphIndexCreator\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.document_loaders import TextLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05d65c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a45a5b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../../state_of_the_union.txt\") as f:\n",
    "    all_text = f.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fca3e1b",
   "metadata": {},
   "source": [
    "We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80522bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\\n\".join(all_text.split(\"\\n\\n\")[105:108])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da5aad5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'It won’t look like much, but if you stop and look closely, you’ll see a “Field of dreams,” the ground on which America’s future will be built. \\nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor “mega site”. \\nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. '"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8dad7b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = index_creator.from_text(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2118f363",
   "metadata": {},
   "source": [
    "We can inspect the created graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32878c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Intel', '$20 billion semiconductor \"mega site\"', 'is going to build'),\n",
       " ('Intel', 'state-of-the-art factories', 'is building'),\n",
       " ('Intel', '10,000 new good-paying jobs', 'is creating'),\n",
       " ('Intel', 'Silicon Valley', 'is helping build'),\n",
       " ('Field of dreams',\n",
       "  \"America's future will be built\",\n",
       "  'is the ground on which')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_triples()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9737be1",
   "metadata": {},
   "source": [
    "## Querying the graph\n",
    "We can now use the graph QA chain to ask question of the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76edc854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import GraphQAChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e7719b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6511169",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new GraphQAChain chain...\u001b[0m\n",
      "Entities Extracted:\n",
      "\u001b[32;1m\u001b[1;3m Intel\u001b[0m\n",
      "Full Context:\n",
      "\u001b[32;1m\u001b[1;3mIntel is going to build $20 billion semiconductor \"mega site\"\n",
      "Intel is building state-of-the-art factories\n",
      "Intel is creating 10,000 new good-paying jobs\n",
      "Intel is helping build Silicon Valley\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Intel is going to build a $20 billion semiconductor \"mega site\" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"what is Intel going to build?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70b9ada",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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